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Volume 4, Issue 2, 2025
Open Access
Research article
Crowd Density Estimation via a VGG-16-Based CSRNet Model
damla tatlıcan ,
nafiye nur apaydin ,
orhan yaman ,
mehmet karakose
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Available online: 04-29-2025

Abstract

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Accurate crowd density estimation has become critical in applications ranging from intelligent urban planning and public safety monitoring to marketing analytics and emergency response. In recent developments, various methods have been used to enhance the precision of crowd analysis systems. In this study, a Convolutional Neural Network (CNN)-based approach was presented for crowd density detection, wherein the Congested Scene Recognition Network (CSRNet) architecture was employed with a Visual Geometry Group (VGG)-16 backbone. This method was applied to two benchmark datasets—Mall and Crowd-UIT—to assess its effectiveness in real-world crowd scenarios. Density maps were generated to visualize spatial distributions, and performance was quantitatively evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics. For the Mall dataset, the model achieved an MSE of 0.08 and an MAE of 0.10, while for the Crowd-UIT dataset, an MSE of 0.05 and an MAE of 0.15 were obtained. These results suggest that the proposed VGG-16-based CSRNet model yields high accuracy in crowd estimation tasks across varied environments and crowd densities. Additionally, the model demonstrates robustness in generalizing across different dataset characteristics, indicating its potential applicability in both surveillance systems and public space management. The outcomes of this investigation offer a promising direction for future research in data-driven crowd analysis, particularly in enhancing predictive reliability and real-time deployment capabilities of deep learning models for population monitoring tasks.

Abstract

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To address the issue of estimating energy consumption in computer systems, this study investigates the contribution of various hardware parameters to energy fluctuations, as well as the correlation between these parameters. Based on this analysis, the CM model was proposed, selecting the most representative and monitorable parameters that reflect changes in system energy consumption. The CMP (Chip Multiprocessors) model adapts to different task states of the computer system by identifying primary components driving energy consumption under varying conditions. Energy consumption estimation was then conducted by monitoring these dominant parameters. Experiments across various task states demonstrate that the CMP model outperforms traditional FAN (Fuzzy Attack Net) and Cubic models, particularly when the computer system engages in data-intensive tasks.

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